Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

ARTICLE Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity Tuomas O. Kilpeläinen et al. # Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1,LHX1,SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1,LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1,LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels. https://doi.org/10.1038/s41467-018-08008-w OPEN Correspondence and requests for materials should be addressed to T.O.K. (email: [email protected]) or to D.C.R. (email: [email protected]) or to R.J.F.L. (email: [email protected]). # A full list of authors and their affiliations appears at the end of the paper. NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications 1 1234567890():,; Circulating levels of blood lipids are strongly linked to the risk of atherosclerotic cardiovascular disease. Regular physical activity (PA) improves blood lipid profile by increasing the levels of high-density lipoprotein cholesterol (HDL-C) and decreasing the levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG)1. However, there is individual variation in the response of blood lipids to PA, and twin studies suggest that some of this variation may be due to genetic differences2. The genes responsible for this variability remain unknown. More than 500 genetic loci have been found to be associated with blood levels of HDL-C, LDL-C, or TG in published genomewide association studies (GWAS)3–12. At present, it is not known whether any of these main effect associations are modified by PA. Understanding whether the impact of lipid loci can be modified by PA is important because it may give additional insight into biological mechanisms and identify subpopulations in whom PA is particularly beneficial. Here, we report results from a genome-wide meta-analysis of gene–PA interactions on blood lipid levels in up to 120,979 adults of European, African, Asian, Hispanic, or Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We show that four loci, in/near CLASP1,LHX1, SNTA1, and CNTNAP2, are associated with circulating lipid levels through interaction with PA. None of these four loci have been identified in published main effect GWAS of lipid levels. The CLASP1,LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of PA interactions in the genetic contribution to blood lipid levels. Results Genome-wide interaction analyses in up to 250,564 individuals. We assessed effects of gene–PA interactions on serum HDL-C, LDL-C, and TG levels in 86 cohorts participating in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Gene-Lifestyle Interactions Working Group13.PA was harmonized across participating studies by categorizing it into a dichotomous variable. The participants were defined as inactive if their reported weekly energy expenditure in moderateto-vigorous intensity leisure-time or commuting PA was less than 225 metabolic equivalent (MET) minutes per week (corresponding to approximately 1 h of moderate-intensity PA), while all other participants were defined as physically active (Supplementary Data 1). The analyses were performed in two stages. Stage 1 consisted of genome-wide meta-analyses of linear regression results from 42 cohorts, including 120,979 individuals of European [n=84,902], African [n=20,487], Asian [n=6403], Hispanic [n=4749], or Brazilian [n=4438] ancestry (Supplementary Tables 1 and 2; Supplementary Data 2; Supplementary Note 1). All variants that reached two-sided P<1×10 −6in the Stage 1 multi-ancestry meta-analyses or ancestry-specific meta-analyses were taken forward to linear regression analyses in Stage 2, which included 44 cohorts and 131,012 individuals of European [n=107,617], African [n=5384], Asian [n=6590], or Hispanic [n=11,421] ancestry (Supplementary Tables 3 and 4; Supplementary Data 3; Supplementary Note 2). The summary statistics from Stage 1 and Stage 2 were subsequently meta-analyzed to identify lipid loci whose effects are modified by PA. We identified lipid loci interacting with PA by three different approaches applied to the meta-analysis of Stage 1 and Stage 2: (i) we screened for genome-wide significant SNP × PA-interaction effects (P INT <5×10 −8); (ii) we screened for genome-wide significant 2 degree of freedom (2df) joint test of SNP main effect and SNP × PA interaction14 (P JOINT <5×10 −8); and (iii) we screened all previously known lipid loci3–12 for significant SNP × PA-interaction effects, Bonferroni-correcting for the number of independent variants tested (r2< 0.1 within 1 Mb distance; P INT =0.05/501 =1.0 × 10−4). PA modifies the effect of four loci on lipid levels. Three novel loci (>1 Mb distance and r2< 0.1 with any previously identified lipid locus) were identified: in CLASP1 (rs2862183, P INT =8×10 −9), near LHX1 (rs295849, P INT =3×10 −8), and in SNTA1 (rs141588480, P INT =2×10 −8), which showed a genomewide significant SNP × PA interaction on HDL-C in all ancestries combined (Table 1, Figs. 1–4). Higher levels of PA enhanced the HDL cholesterol-increasing effects of the CLASP1,LHX1, and SNTA1 loci. A novel locus in CNTNAP2 (rs190748049) was genome-wide significant in the joint test of SNP main effect and SNP × PA interaction (P JOINT =4×10 −8) and showed moderate evidence of SNP × PA interaction (P INT =2×10 −6) in the metaanalysis of LDL-C in all ancestries combined (Table 1, Fig. 5). The LDL-C-increasing effect of the CNTNAP2 locus was attenuated in the physically active group as compared to the inactive group. None of these four loci have been identified in previous main effect GWAS of lipid levels. No interaction between known main effect lipid loci and PA. Of the previously known 260 main effect loci for HDL-C, 202 for LDL-C, and 185 for TG3–12, none reached the Bonferronicorrected threshold (two-sided P INT =1.0 × 10−4) for SNP × PA interaction alone (Supplementary Data 4-6). We also found no significant interaction between a combined score of all published European-ancestry loci for HDL-C, LDL-C, or TG with PA (Supplementary Datas 7–9) using our European-ancestry summary results (two-sided P HDL-C =0.14, P LDL-C =0.77, and P TG = 0.86, respectively), suggesting that the beneficial effect of PA on lipid levels may be independent of genetic risk15. Potential functional roles of the loci interacting with PA. While the mechanisms underlying the beneficial effect of PA on circulating lipid levels are not fully understood, it is thought that the changes in plasma lipid levels are primarily due to an improvement in the ability of skeletal muscle to utilize lipids for energy due to enhanced enzymatic activities in the muscle16,17. Of the four loci we found to interact with PA, three, in CLASP1, near LHX1, and in SNTA1, harbor genes that may play a role in muscle function18,19 and lipid metabolism20,21. The lead variant rs2862183 (minor allele frequency (MAF) 22%) in the CLASP1 locus which interacts with PA on HDL-C levels is an intronic SNP in CLASP1 that encodes a microtubuleassociated protein (Fig. 2). The rs2862183 SNP is associated with CLASP1 expression in esophagus muscularis (P=3×10 −5) and is in strong linkage disequilibrium (r2> 0.79) with rs13403769 variant that shows the strongest association with CLASP1 expression in the region (P=7×10 −7). Another potent causal candidate gene in this locus is the nearby GLI2 gene which has been found to play a role in skeletal myogenesis18 and the conversion of glucose to lipids in mouse adipose tissue20 by inhibiting hedgehog signaling. The rs295849 (MAF 38%) variant near LHX1 interacts with PA on HDL-C levels. However, the more likely causal candidate gene in this locus is acetyl-CoA carboxylase (ACACA), which plays a crucial role in fatty acid metabolism21 (Fig. 3). Rare acetyl-CoA carboxylase deficiency has been linked to hypotonic myopathy, severe brain damage, and poor growth22. The lead variant in the SNTA1 locus (rs141588480) interacts with PA on HDL-C and is an insertion only found in individuals ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w 2NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications of African (MAF 6%) or Hispanic (MAF 1%) ancestry. The rs141588480 insertion is in the SNTA1 gene that encodes the syntrophin alpha 1 protein, located at the neuromuscular junction and altering intracellular calcium ion levels in muscle tissue (Fig. 4). Snta1-null mice exhibit differences in muscle regeneration after a cardiotoxin injection19. Two weeks following the injection into mouse tibialis anterior, the muscle showed hypertrophy, decreased contractile force, and neuromuscular junction dysfunction. Furthermore, exercise endurance of the mice was impaired in the early phase of muscle regeneration19.In humans, SNTA1 mutations have been linked to the long-QT syndrome23. The fourth locus interacting with PA is CNTNAP2, with the lead variant (rs190748049) intronic and no other genes nearby (Fig. 5). The rs190748049 variant is most common in Africanancestry (MAF 8%), less frequent in European-ancestry (MAF 2%), and absent in Asianand Hispanic-ancestry populations. The protein coded by the CNTNAP2 gene, contactin-associated protein like-2, is a member of the neurexin protein family. The protein is located at the juxtaparanodes of myelinated axons where it may have an important role in the differentiation of the axon into specific functional subdomains. Mice with a Cntnap2 knockout are used as an animal model of autism and show altered phasic inhibition and a decreased number of interneurons24. Human CNTNAP2 variants have been associated with risk of autism and related behavioral disorders25. Joint test of SNP main effect and SNP × PA interaction.We found 101 additional loci that reached genome-wide significance in the 2df joint test of SNP main effect and SNP × PA interaction on HDL-C, LDL-C, or TG. However, none of these loci showed evidence of SNP × PA interaction (P INT > 0.001) (Supplementary Data 10). All 101 main effect-driven loci have been identified in previous GWAS of lipid levels3–12. Discussion In this genome-wide study of up to 250,564 adults from diverse ancestries, we found evidence of interaction with PA for four loci, in/near CLASP1,LHX1,SNTA1, and CNTNAP2. Higher levels of PA enhanced the HDL cholesterol-increasing effects of CLASP1, LHX1, and SNTA1 loci and attenuated the LDL cholesterolincreasing effect of the CNTNAP2 locus. None of these four loci have been identified in previous main effect GWAS for lipid levels3–12. The loci in/near CLASP1,LHX1, and SNTA1 harbor genes linked to muscle function18,19 and lipid metabolism20,21. More specifically, the GLI2 gene within the CLASP1 locus has been found to play a role in myogenesis18 as well as in the conversion of glucose to lipids in adipose tissue20; the ACACA gene within the LHX1 locus plays a crucial role in fatty acid metabolism21 and has been connected to hypotonic myopathy22; and the SNTA1 gene is linked to muscle regeneration19. These functions may relate to differences in the ability of skeletal muscle to use lipids as an energy source, which may modify the beneficial impact of PA on blood lipid levels16,17. The inclusion of diverse ancestries in the present meta-analyses allowed us to identify two loci that would have been missed in meta-analyses of European-ancestry individuals alone. In particular, the lead variant (rs141588480) in the SNTA1 locus is only polymorphic in African and Hispanic ancestries, and the lead CLASP1 LHX1 SNTA1 5e–08 –log10 (P-value) 8 6 4 2 0 chr1 chr2 chr3 chr4 chr5 chr6 chr22 chr21 chr20 chr19 chr18 chr17 chr16 chr15 chr14 chr13 chr12 chr11 chr10 chr9 chr8 chr7 Chromosome Fig. 1 Genome-wide results for interaction with physical activity on HDL cholesterol levels. The Pvalues are two-sided and were obtained by a meta-analysis of linear regression model results (nup to 250,564). Three loci, in/near CLASP1,LHX1,andSNTA1, reached genome-wide significance (P<5×10 −8)as indicated in the plot Table 1 Lipid loci identified through interaction with physical activity (P INT <5×10 −8) or through joint test for SNP main effect and SNP × physical activity interaction (P JOINT <5×10 −8) Trait SNP Chr:Pos Gene EA/OA EAF Ninactive Nactive Beta INT se INT P INT P JOINT Loci identified through interaction with physical activity HDL-C rs2862183 2:122415398 CLASP1 T/C 0.22 76,674 154,118 0.014 0.003 7.5E−93.6E−7 HDL-C rs295849 17:35161748 LHX1 T/G 0.38 78,288 160,924 0.009 0.002 2.7E−86.8E−7 HDL-C rs141588480 20:32013913 SNTA1 Ins/Del 0.95 8,694 18,585 0.054 0.010 2.0E−86.1E−7 Loci identified through joint test for SNP main effect and SNP × physical activity interaction LDL-C rs190748049 7:146418260 CNTNAP2 C/T 0.95 14,912 28,715 −7.2 1.5 1.6E−64.2E−8 All loci were identified in the meta-analyses of all ancestries combined. HDL-C was natural logarithmically transformed, whereas LDL-C was not transformed. The Pvalues are two-sided and were obtained using a meta-analysis of linear regression model results. EA effect allele, EAF effect allele frequency, OA other allele, beta INT effect size for interaction with physical activity (=the change in logarithmically transformed HDL-C or untransformed LDL-C levels in the active group as compared to the inactive group per each effect allele), se INT standard error for interaction with physical activity NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications 3 variant (rs190748049) in the CNTNAP2 locus is four times more frequent in African-ancestry than in European-ancestry. Our findings highlight the importance of multi-ancestry investigations of gene-lifestyle interactions to identify novel loci. We did not find additional novel loci when jointly testing for SNP main effect and interaction with PA. While 101 loci reached genome-wide significance in the joint test on HDL-C, LDL-C, or TG, all of these loci have been identified in previous GWAS of lipid levels3–12, and none of them showed evidence of SNP × PA interaction. The 2df joint test bolsters the power to detect novel loci when both main and an interaction effect are present14. The lack of novel loci identified by the 2df test suggests that the loci Asian (n = 4209) Brazilian (n = 4438) African (n = 20,118) Hispanic (n = 4308) Hispanic (n = 11,421) African (n = 5384) European (n = 100,936) Asian (n = 4732) European (n = 83,666) Stage 2: P = 0.013 l2=0%, n = 122,473 Stage 1: P = 1.3E–7 l2=42%, n = 116,739 Stage 1+2: P = 2.3E–8 l2=0%, n = 239,212 –0.06 –0.04 –0.02 0.02 0.04 0.06 34.5 10 8 6 4 2 0 TAF1 5 CCL3L3 CCL3L1 TBC1D3H CCL4L1 CCL4L2 LOC101060321 TBCD3F TBC1D3B MRM1 DHRS11 GGNBP2 LHX1 AATF MIR2909 ACACA SYNRG DDX52 HNF1B C17orf78 TADA 2A DUSP14 MIR378J PIGW MYO19 ZNHIT3 HEATR9 CCL5 CCL4 RDM1 LYZ L 6 CCL16 CCL14 CCL15–CCL14 CCL15 CCL23 35 35.5 36 Position on chr17 (Mb) 0 Beta (mg/dL per G allele) Locus near LHX1 100 80 60 40 20 Recombination rate (cM/Mb) 0 rs295849 0.2 0.4 0.6 0.8 r2 –log10 (p-value) ab Fig. 3 Interaction of rs295849 near LHX1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a)is shown for the rs295849 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each G allele of rs295849. The −log 10 (Pvalue) in the association plot (b) is also shown for the rs295849 × physical activity interaction term. The Pvalues are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org) –0.12 Brazilian (n = 4438) Locus in CLASP1 Asian (n = 3654) Asian (n = 2293) Hispanic (n = 4308) Hispanic (n = 10,479) African (n = 20,118) African (n = 5099) European (n = 82,554) European (n = 97,564) Stage 1: P = 2.4E–7 l2=0%, n = 115,072 Stage 2: P = 0.0065 l2=0%, n = 115,720 Stage 1+2: P = 7.5E–9 l2=0%, n = 230,792 –0.1 –0.08 –0.06 –0.04 –0.02 0.02 Beta (mg/dL per T allele) 0.04 0.06 121.5 GLl2 TFCP2L1 RNU4ATAC TSN NIFK–AS1 CLASP1 10 100 80 60 40 20 Recombination (cM/Mb) 0 rs2862183 0.2 0.4 0.6 0.8 r2 8 6 4 2 0 122 122.5 123 Position on chr2 (Mb) –log10 (p-value) 0 ab Fig. 2 Interaction of rs2862183 in CLASP1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a)is shown for the rs2862183 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each T allele of rs2862183. The −log 10 (Pvalue) in the association plot (b) is also shown for the rs2862183 × physical activity interaction term. The Pvalues are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org) ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w 4NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications showing the strongest SNP × PA interaction on lipid levels are not the same loci that show a strong main effect on lipid levels. In summary, we identified four loci containing SNPs that enhance the beneficial effect of PA on lipid levels. The identification of the SNTA1 and CNTNAP2 loci interacting with PA was made possible by the inclusion of diverse ancestries in the analyses. The gene regions that harbor loci interacting with PA involve pathways targeting muscle function and lipid metabolism. Our findings elucidate the role and underlying mechanisms of PA interactions in the genetic regulation of blood lipid levels. 100 80 60 rs190748049 40 20 0 Locus in CNTNAP2 10 0 6 4 2 0 –log10 (p-value) Recombination rate (cM/Mb) Position on chr7 (Mb) 145.5 146 146.5 147 Beat (log-mg/dL per T allele) European (n = 21,518) African (n = 17,434) Stage 1: P = 8.2E–6 I2 = 74%, n = 38,952 European (n = 3264) African (n = 1411) Stage 2: P = 0.067 I2 = 0%, n = 4675 Stage 1+2: P = 1.6E–6 I2 = 0%, n = 43,627 1050–5–15–25 –10–20–30–40 –35 r2 0.8 0.6 0.4 0.2 LOC105375556 MIR548F4 LOC101928700 CNTNAP2 ab Fig. 5 Interaction of rs190748049 variant in CNTNAP2 with physical activity on LDL cholesterol levels. The rs190748049 variant was genome-wide significant in the joint test for SNP main effect and SNP × physical activity interaction and reached P=2×10 −6for the SNP × physical activity interaction term alone. The beta and 95% confidence intervals in the forest plot (a) is shown for the SNP × physical activity interaction term, i.e., it indicates the decrease in LDL cholesterol levels in the active group as compared to the inactive group per each T allele of rs190748049. The −log 10 (Pvalue) in the association plot (b) is also for the SNP × physical activity interaction term. The Pvalues are two-sided and were obtained using a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org) African (n = 16,800) Locus in SNTA1 Hispanic (n = 10,479) Stage 1: P = 1.3E–7 n = 16,800 Stage 2: P = 0.045 n = 10,479 Stage 1+2, P = 2.0E–8 l2 = 0%, n = 27,279 Beat (mg/dL per insertion) 0.14–0.04 –0.02 0.02 0.04 0.06 0.08 0.1 0.120 –log10 (p-value) Recombination rate (cM/Mb) 100 r2 0.8 0.6 0.4 0.2 80 60 40 20 0 10 8 6 4 2 0 Position on chr20 (Mb) rs141588480 31.5 32.5 3332 ASXL1 NOL4L MAPRE1 DNMT3B BPIFB2 BPIFA1 CBFA2T2 CHMP4B EIF2S2 ITCH ASIPRALY-AS1 RALY MIR4755 AHCY BPIFB3 BPIFB1 CDK5RAP1 ACTL10 E2F1 PXMP4 ZNF341 ZNF341-AS1 C20orf144 NECAB3 SNTA1 BPIFA3SUN5LOC101929698 BPIFB6 BPIFB4 BPIFA2 BPIFA4P LOC149950 C20orf203 COMMD7 ab Fig. 4 Interaction of rs141588480 in SNTA1 with physical activity on HDL cholesterol levels. The beta and 95% confidence intervals in the forest plot (a)is shown for the rs141588480 × physical activity interaction term, i.e., it indicates the increase in logarithmically transformed HDL cholesterol levels in the active group as compared to the inactive group per each insertion of rs141588480. The –log 10 (pvalue) in the association plot (b) is also shown for the rs141588480 × physical activity interaction term. While the rs141588480 variant was identified in African-ancestry individuals in Stage 1, the variant did not pass QC filters in the Stage 2 African-ancestry cohorts, due to insufficient sample sizes of these cohorts. The Pvalues are two-sided and were obtained by a meta-analysis of linear regression model results. The figure was generated using LocusZoom (http://locuszoom.org) NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications 5 Methods Study design. The present study collected summary data from 86 participating cohorts and no individual-level data were exchanged. For each of the participating cohorts, the appropriate ethics review board approved the data collection and all participants provided informed consent. We included men and women 18–80 years of age and of European, African, Asian, Hispanic, or Brazilian ancestry. The meta-analyses were performed in two stages13. Stage 1 meta-analyses included 42 studies with a total of 120,979 individuals of European (n=84,902), African (n=20,487), Asian (n=6403), Hispanic (n=4749), or Brazilian ancestry (n=4438) (Supplementary Table 1; Supplementary Data 2; Supplementary Note 1). Stage 2 meta-analyses included 44 studies with a total of 131,012 individuals of European (n=107,617), African (n=5384), Asian (n=6590), or Hispanic (n=11,421) ancestry (Supplementary Table 3; Supplementary Data 3; Supplementary Note 2). Studies participating in Stage 1 meta-analyses carried out genome-wide analyses, whereas studies participating in Stage 2 only performed analyses for 17,711 variants that reached P<10 −6in the Stage 1 meta-analyses and were observed in at least two different Stage 1 studies with a pooled sample size > 4000. The Stage 1 and Stage 2 metaanalyses were performed in all ancestries combined and in each ancestry separately. Outcome traits: LDL-C, HDL-C, and TG. The levels of LDL-C were either directly assayed or derived using the Friedewald equation (if TG ≤400 mg dl−1and fasting ≥8 h). We adjusted LDL-C levels for lipid-lowering drug use if statin use was reported or if unspecified lipid-lowering drug use was listed after 1994, when statin use became common. For directly assayed LDL-C, we divided the LDL-C value by 0.7. If LDL-C was derived using the Friedewald equation, we first adjusted total cholesterol for statin use (total cholesterol divided by 0.8) before the usual calculation. If study samples were from individuals who were nonfasting, we did not include either TG or calculated LDL-C in the present analyses. The HDL-C and TG variables were natural log-transformed, while LDL-C was not transformed. PA variable. The participating studies used a variety of ways to assess and quantify PA (Supplementary Data 1). To harmonize the PA variable across all participating studies, we coded a dichotomous variable, inactive vs. active, that could be applied in a relatively uniform way in all studies, and that would be congruent with previous findings on SNP × PA interactions26–28 and the relationship between PA and disease outcomes29. Inactive individuals were defined as those with <225 METmin per week of moderate-to-vigorous leisure-time or commuting PA (n=84,495; 34% of all participants) (Supplementary Data 1). We considered all other participants as physically active. In studies where MET-min per week measures of PA were not available, we defined inactive individuals as those engaging in ≤1 h/week of moderate-intensity leisure-time PA or commuting PA. In studies with PA measures that were not comparable to either MET-min or hours/week of PA, we defined the inactive group using a percentage cut-off, where individuals in the lowest 25% of PA levels were defined as inactive and all other individuals as active. Genotyping and imputation. Genotyping was performed by each participating study using Illumina or Affymetrix arrays. Imputation was conducted on the cosmopolitan reference panel from the 1000 Genomes Project Phase I Integrated Release Version 3 Haplotypes (2010–2011 data freeze, 2012-03-14 haplotypes). Only autosomal variants were considered. Specific details of each participating study’s genotyping platform and imputation software are described in Supplementary Tables 2 and 4. Quality control. The participating studies excluded variants with MAF < 1%. We performed QC for all study-specific results using the EasyQC package in R30. For each study-specific results file, we filtered out genetic variants for which the product of minor allele count (MAC) in the inactive and active strata and imputation quality [min(MAC INACTIVE ,MAC ACTIVE ) × imputation quality] did not reach 20. This removed unstable study-specific results that reflected small sample size, low MAC, or low-imputation quality. In addition, we excluded all variants with imputation quality measure <0.5. To identify issues with relatedness, we examined QQ plots and genomic control inflation lambdas in each study-specific results file as well as in the meta-analysis results files. To identify issues with allele frequencies, we compared the allele frequencies in each study file against ancestry-specific allele frequencies in the 1000 Genomes reference panel. To identify issues with trait transformation, we plotted the median standard error against the maximal sample size in each study. The summary statistics for all beta-coefficients, standard errors, and Pvalues were visually compared to observe discrepancies. Any issues that were found during the QC were resolved by contacting the analysts from the participating studies. Additional details about QC in the context of interactions, including examples, may be found elsewhere13. Analysis methods. All participating studies used the following model to test for interaction: EY½¼β0þβEPA þβGGþβINT GPA þβcC; where Yis the HDL-C, LDL-C, or TG value, PA is the PA variable with 0 or 1 coding for active or inactive group, and Gis the dosage of the imputed genetic variant coded additively from 0 to 2. The Cis the vector of covariates which included age, sex, study center (for multi-center studies), and genome-wide principal components. From this model, the studies provided the estimated genetic main effect (β G ), estimated interaction effect (β GE ), and a robust estimate of the covariance between β G and β GE . Using these estimates, we performed inverse variance-weighted meta-analyses for the SNP × PA interaction term alone, and 2df joint meta-analyses of the SNP effect and SNP × PA interaction combined by the method of Manning et al.14, using the METAL meta-analysis software. We applied genomic control correction twice in Stage 1, first for study-specific GWAS results and again for meta-analysis results, whereas genomic control correction was not applied to the Stage 2 results as interaction testing was only performed at select variants. We considered a variant that reached two-sided P<5×10 −8in the metaanalysis for the interaction term alone or in the joint test of SNP main effect and SNP × PA interaction, either in the ancestry-specific analyses or in all ancestries combined, as genome-wide significant. The loci were defined as independent if the distance between the lead variants was >1 Mb. Combined PA-interaction effect of all known lipid loci. To identify all published SNPs associated with HDL-C, LDL-C, or TG, we extended the previous curated list of lipid loci by Davis et al.4by searching PubMed and Google Scholar databases and screening the GWAS Catalog. After LD pruning by r2< 0.1 in the 1000 Genomes European-ancestry reference panel, 260 independent loci remained associated with HDL cholesterol, 202 with LDL cholesterol, and 185 with TG (Supplementary Datas 7–9). To approximate the combined PA interaction of all known European-ancestry loci associated with HDL-C, LDL-C, or TG, we calculated their combined interaction effect as the weighted sum of the individual SNP coefficients in our genome-wide summary results for European-ancestry. This approach has been described previously in detail by Dastani et al.31 and incorporated in the package “gtx”in R. We did not weigh the loci by their main effect estimates from the discovery GWAS data. Examining the functional roles of loci interacting with PA. We examined published associations of the identified lipid loci with other complex traits in genome-wide association studies by using the GWAS Catalog of the European Bioinformatics Institute and the National Human Genome Research Institute. We extracted all published genetic associations with r2> 0.5 and distance < 500 kb from the identified lipid-associated lead SNPs32. We also studied the cis-associations of the lead SNPs with all genes within ±1 Mb distance using the GTEx portal33.We excluded findings where our lead SNP was not in strong LD (r2> 0.5) with the peak SNP associated with the same gene transcript. Data availability The meta-analysis summary results are available for download on the CHARGE dbGaP website under accession phs000930. Received: 6 June 2018 Accepted: 7 December 2018 References 1. Leon, A. S. & Sanchez, O. A. Response of blood lipids to exercise training alone or combined with dietary intervention. Med. Sci. 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Nature 550, 204–213 (2017). Acknowledgments The present work was largely supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). The full list of acknowledgments appears in the Supplementary Notes 3 and 4. Author contributions T.O.K., K. Schwander., D.C.R., and R.J.F.L. conceived and designed the study. The members of the writing group were T.O.K., A.R.B., R.N., Y.J.S., K.Schwander., T. Winkler, H.J., D.I.C., A. Manning., I.N., B.M.P., K.R., P.B.M., M.F., L.A.C., C.N.R., A. C.M., D.C.R., and R.J.F.L. The genome-wide association results were provided by A.R.B., R.N., Y.J.S., K.Strauch, T. Winkler, D.I.C., A. Manning., I.N., H.A., M.R.B., L.d.l.F., N.F., X.G., D.V., S.A., M.F.F., M.K., S.K.M., M. Richard, H.W., Z.W., T.M.B., L.F.B., A.C., R.D., V.F., F.P.H., A.R.V.R.H., C. Li, K.K.L., J.M., X.S., A.V.S., S.M.T., M. Alver., M. Amini, M. Boissel, J.F.C., X.C., J. Divers, E.E., C. Gao, M. Graff, S.E.H., M.H., F.C.H., A.U.J., J.H.Z., A.T.K., B.K., F.L., L.P.L., I.M.N., R. Rauramaa., M. Riaz, A.R., R. Rueedi, H.M.S., F.T., P.J. v.d.M., T.V.V., N.V., E.B.W., W.W., X.L., L.R.Y., N.A., D.K.A., E.B., M. Brumat, B.C., M.C., Y.D.I.C., M.P.C., J.C., R.d.M., H.J.d.S., P.S.d.V., A.D., J. Ding, C.B.E., J.D.F., Y.F., K.P.G., M. Ghanbari, F.G., C.C.G., D.G., T.B.H., J.H., S.H., C.K.H., S.C.H., A.I., J.B.J., W.P.K., P.K., J.E.K., S.B.K., Z.K., J.K., C.D.L., C. Langenberg, L.J.L., K.L., R.N.L., C.E.L., J. Liang, J. Liu, R.M., A. Manichaikul, T.M., A. Metspalu, Y.M., K.L.M., T.H.M., A.D.M., M.A.N., E.E.K.N., C.P.N., S.N., J.M.N., J.O., N.D.P., G.J.P., R.P., N.L.P., A. Peters, P.A.P., O.P., D.J.P., A. Poveda, O.T.R., S.S.R., N.R., J.G.R., L.M.R., I.R., P.J.S., R.A.S., S.S.S., M.S., J.A.S., H.S., T.S., J.M.S., B.S., K.St., H.T., K.D.T., M.Y.T., J.T., A.G.U., M.Y.v.d.E., D.v.H., T.V., M.W., P.W., G.W., Y.B.X., J.Y., C.Y., J.M.Y., W. Zhao, A.B.Z., D.M.B., M. Boehnke, D.W.B., U.d.F., I.J.D., P.E., T.E., B.I.F., P.F., P.G., C. Gieger, N.K., M.L., T.A.L., T.L., P.K.E.M., A.J.O., B.W.J.H.P., N.J.S., X.O.S., P.v.d.H., J.V.V.V.O., P.V., L.E.W., Y.X.W., N.J.W., D.R.W., T. Wu, W. Zheng, X.Z., M.K.E., P.W.F., V.G., C.H., B.L.H., T.N.K., Y.L., K.E.N., A.C.P., P.M.R., E.S.T., R.M.v.D., E.R.F., S.L.R.K., C.T.L., D.O.M.K., M.A.P., S.R., C.M.v.D., J.I.R., C.B.K., W.J.G., B.M.P., K.R., P.B.M., M.F., L.A.C., C.N.R., A.C.M., D.C.R., and R.J.F.L.; The meta-analyses were performed by T.O.K. and H.J.; The combined physical activity interaction effects of all known lipid loci were examined by T.O.K. and H.J.; T.O.K. and C.V.N. collected look-up information in GWAS studies for other traits; T.O.K. and C.V.N. carried out the eQTL look-ups. All authors reviewed and approved the final manuscript. Additional information Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467018-08008-w. Competing interests: Bruce M. Psaty serves on the DSMB of a clinical trial funded by the manufacturer (Zoll LifeCor) and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Brenda W.J.H. Penninx has received research funding (nonrelated to the work reported here) from Jansen Research and Boehringer Ingelheim. Mike A. Nalls’participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. Dr. Nalls also consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare among others, and has a Commercial affiliation with Data Technica International, Glen Echo, MD, USA. Jost B. Jonas serves as a consultant for Mundipharma Co. (Cambridge, UK), patent holder with Biocompatibles UK Ltd. (Franham, Surrey, UK) (Title: Treatment of eye diseases using encapsulated cells encoding and secreting neuroprotective factor and/or anti-angiogenic factor; Patent number: 20120263794), and is patent applicant with University of Heidelberg (Heidelberg, Germany) (Title: Agents for use in the therapeutic or prophylactic treatment of myopia or hyperopia; Europäische Patentanmeldung 15,000 771.4). Paul W. Franks has been a paid consultant in the design of a personalized Nutrition trial (PREDICT) as part of a private-public partnership at Kings College London, UK, and has received research support from several pharmaceutical Companies as part of European Union Innovative Medicines Initiative (IMI) Projects. Terho Lehtimäki is employed by Fimlab Ltd. Ozren Polasek is employed by Gen-info Ltd. The remaining authors declare no competing interests. Reprints and permission information is available online at http://npg.nature.com/ reprintsandpermissions/ Journal peer review information: Nature Communications thanks David Meyre and the other anonymous Reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/. © The Author(s) 2019 NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications 7 Tuomas O. Kilpeläinen 1,2 , Amy R. Bentley 3 , Raymond Noordam 4 , Yun Ju Sung 5 , Karen Schwander 5 , Thomas W. Winkler 6 , Hermina Jakupović 1 , Daniel I. Chasman 7,8 , Alisa Manning 9,10 , Ioanna Ntalla 11 , Hugues Aschard 12,13 , Michael R. Brown 14 , Lisa de las Fuentes 5,15 , Nora Franceschini 16 , Xiuqing Guo 17 , Dina Vojinovic 18 , Stella Aslibekyan 19 , Mary F. Feitosa 20 , Minjung Kho 21 , Solomon K. Musani 22 , Melissa Richard 23 , Heming Wang 24 , Zhe Wang 14 , Traci M. Bartz 25 , Lawrence F. Bielak 21 , Archie Campbell 26 , Rajkumar Dorajoo 27 , Virginia Fisher 28 , Fernando P. Hartwig 29,30 , Andrea R.V.R. Horimoto 31 , Changwei Li 32 , Kurt K. Lohman 33 , Jonathan Marten 34 , Xueling Sim 35 , Albert V. Smith 36,37 , Salman M. Tajuddin 38 , Maris Alver 39 , Marzyeh Amini 40 , Mathilde Boissel 41 , Jin Fang Chai 35 , Xu Chen 42 , Jasmin Divers 43 , Evangelos Evangelou 44,45 , Chuan Gao 46 , Mariaelisa Graff 16 , Sarah E. Harris 26,47 , Meian He 48 , Fang-Chi Hsu 43 , Anne U. Jackson 49 , Jing Hua Zhao 50 , Aldi T. Kraja 20 , Brigitte Kühnel 51,52 , Federica Laguzzi 53 , Leo-Pekka Lyytikäinen 54,55 , Ilja M. Nolte 40 , Rainer Rauramaa 56 , Muhammad Riaz 57 , Antonietta Robino 58 , Rico Rueedi 59,60 , Heather M. Stringham 49 , Fumihiko Takeuchi 61 , Peter J. van der Most 40 , Tibor V. Varga 62 , Niek Verweij 63 , Erin B. Ware 64 , Wanqing Wen 65 , Xiaoyin Li 66 , Lisa R. Yanek 67 , Najaf Amin 18 , Donna K. Arnett 68 , Eric Boerwinkle 14,69 , Marco Brumat 70 , Brian Cade 24 , Mickaël Canouil 41 , Yii-Der Ida Chen 17 , Maria Pina Concas 58 , John Connell 71 , Renée de Mutsert 72 , H. Janaka de Silva 73 , Paul S. de Vries 14 ,Ayşe Demirkan 18 , Jingzhong Ding 74 , Charles B. Eaton 75 , Jessica D. Faul 64 , Yechiel Friedlander 76 , Kelley P. Gabriel 77 , Mohsen Ghanbari 18,78 , Franco Giulianini 7 , Chi Charles Gu 5 , Dongfeng Gu 79 , Tamara B. Harris 80 , Jiang He 81,82 , Sami Heikkinen 83,84 , Chew-Kiat Heng 85,86 , Steven C. Hunt 87,88 , M. Arfan Ikram 18,89 , Jost B. Jonas 90,91 , Woon-Puay Koh 35,92 , Pirjo Komulainen 56 , Jose E. Krieger 31 , Stephen B. Kritchevsky 74 , Zoltán Kutalik 60,93 , Johanna Kuusisto 84 , Carl D. Langefeld 43 , Claudia Langenberg 50 , Lenore J. Launer 80 , Karin Leander 53 , Rozenn N. Lemaitre 94 , Cora E. Lewis 95 , Jingjing Liang 66 , Lifelines Cohort Study, Jianjun Liu 27,96 , Reedik Mägi 39 , Ani Manichaikul 97 , Thomas Meitinger 98,99 , Andres Metspalu 39 , Yuri Milaneschi 100 , Karen L. Mohlke 101 , Thomas H. Mosley Jr. 102 , Alison D. Murray 103 , Mike A. Nalls 104,105 , Ei-Ei Khaing Nang 35 , Christopher P. Nelson 106,107 , Sotoodehnia Nona 108 , Jill M. Norris 109 , Chiamaka Vivian Nwuba 1 , Jeff O’Connell 110,111 , Nicholette D. Palmer 112 , George J. Papanicolau 113 , Raha Pazoki 44 , Nancy L. Pedersen 42 , Annette Peters 52,114 , Patricia A. Peyser 21 , Ozren Polasek 115,116,117 , David J. Porteous 26,47 , Alaitz Poveda 62 , Olli T. Raitakari 118,119 , Stephen S. Rich 97 , Neil Risch 120 , Jennifer G. Robinson 121 , Lynda M. Rose 7 , Igor Rudan 122 , Pamela J. Schreiner 123 , Robert A. Scott 50 , Stephen S. Sidney 124 , Mario Sims 22 , Jennifer A. Smith 21,64 , Harold Snieder 40 , Tamar Sofer 10,24 , John M. Starr 47,125 , Barbara Sternfeld 124 , Konstantin Strauch 126,127 , Hua Tang 128 , Kent D. Taylor 17 , Michael Y. Tsai 129 , Jaakko Tuomilehto 130,131 , André G. Uitterlinden 132 , M. Yldau van der Ende 63 , Diana van Heemst 4 , Trudy Voortman 18 , Melanie Waldenberger 51,52 , Patrik Wennberg 133 , Gregory Wilson 134 , Yong-Bing Xiang 135 , Jie Yao 17 , Caizheng Yu 48 , Jian-Min Yuan 136,137 , Wei Zhao 21 , Alan B. Zonderman 138 , Diane M. Becker 67 , Michael Boehnke 49 , Donald W. Bowden 112 , Ulf de Faire 53 , Ian J. Deary 47,139 , Paul Elliott 44,140 , Tõnu Esko 39,141 , Barry I. Freedman 142 , Philippe Froguel 41,143 , Paolo Gasparini 58,70 , Christian Gieger 51,144 , Norihiro Kato 61 , Markku Laakso 84 , Timo A. Lakka 56,83,145 , Terho Lehtimäki 54,55 , Patrik K.E. Magnusson 42 , Albertine J. Oldehinkel 146 , Brenda W.J.H. Penninx 100 , Nilesh J. Samani 106,107 , Xiao-Ou Shu 65 , Pim van der Harst 63,147,148 , Jana V. Van Vliet-Ostaptchouk 149 , Peter Vollenweider 150 , Lynne E. Wagenknecht 151 , Ya X. Wang 91 , Nicholas J. Wareham 50 , David R. Weir 64 , Tangchun Wu 48 , Wei Zheng 65 , Xiaofeng Zhu 66 , Michele K. Evans 38 , Paul W. Franks 62,133,152,153 , Vilmundur Gudnason 36,154 , Caroline Hayward 34 , Bernardo L. Horta 29 , Tanika N. Kelly 81 , Yongmei Liu 155 , Kari E. North 16 , Alexandre C. Pereira 31 , Paul M. Ridker 7,8 , E. Shyong Tai 35,92,156 , Rob M. van Dam 35,156 , ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w 8NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications Ervin R. Fox 157 , Sharon L.R. Kardia 21 , Ching-Ti Liu 28 , Dennis O. Mook-Kanamori 72,158 , Michael A. Province 20 , Susan Redline 24 , Cornelia M. van Duijn 18 , Jerome I. Rotter 17 , Charles B. Kooperberg 159 , W. James Gauderman 160 , Bruce M. Psaty 124,161 , Kenneth Rice 162 , Patricia B. Munroe 11,163 , Myriam Fornage 23 , L. Adrienne Cupples 28,164 , Charles N. Rotimi 3 , Alanna C. Morrison 14 , Dabeeru C. Rao 5 & Ruth J.F. Loos 165,166 1 Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark. 2 Department of Environmental Medicine and Public Health, The Icahn School of Medicine at Mount Sinai, New York 10029 NY, USA. 3 Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda 20892 MD, USA. 4 Internal Medicine, Gerontology and Geriatrics, Leiden University Medical Center, Leiden 2300 RC, The Netherlands. 5 Division of Biostatistics, Washington University School of Medicine, St. Louis 63110 MO, USA. 6 Department of Genetic Epidemiology, University of Regensburg, Regensburg 93051, Germany. 7 Preventive Medicine, Brigham and Women’s Hospital, Boston 02215 MA, USA. 8 Harvard Medical School, Boston 02131 MA, USA. 9 Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston 02114 MA, USA. 10 Department of Medicine, Harvard Medical School, Boston 02115 MA, USA. 11 Clinical Pharmacology, William Harvey Research Instititute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London EC1M 6BQ, UK. 12 Department of Epidemiology, Harvard School of Public Health, Boston 02115 MA, USA. 13 Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI), Institut Pasteur, Paris 75015, France. 14 Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston 77030 TX, USA. 15 Cardiovascular Division, Department of Medicine, Washington University, St. Louis 63110 MO, USA. 16 Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill 27514 NC, USA. 17 The Institute for Translational Genomics and Population Sciences, Division of Genomic Outcomes, Department of Pediatrics, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, Torrance 90502 CA, USA. 18 Department of Epidemiology, Erasmus University Medical Center, Rotterdam 3015 CE, The Netherlands. 19 Department of Epidemiology, University of Alabama at Birmingham, Birmingham 35294 AL, USA. 20 Division of Statistical Genomics, Department of Genetics, Washington University School of Medicine, St. Louis 63108 MO, USA. 21 Department of Epidemiology, School of Public Health, University of Michigan, Ann Arbor 48109 MI, USA. 22 Jackson Heart Study, Department of Medicine, University of Mississippi Medical Center, Jackson 39213 MS, USA. 23 Institute of Molecular Medicine, McGovern Medical School, University of Texas Health Science Center at Houston, Houston 77030 TX, USA. 24 Division of Sleep and Circadian Disorders, Brigham and Women’s Hospital, Boston 02115 MA, USA. 25 Cardiovascular Health Research Unit, Biostatistics and Medicine, University of Washington, Seattle 98101 WA, USA. 26 Centre for Genomic & Experimental Medicine, Institute of Genetics & Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK. 27 Genome Institute of Singapore, Agency for Science Technology and Research, Singapore 138672, Singapore. 28 Biostatistics, Boston University School of Public Health, Boston 02118 MA, USA. 29 Postgraduate Program in Epidemiology, Federal University of Pelotas, Pelotas 96020220 RS, Brazil. 30 Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol BS8 2BN, UK. 31 Laboratory of Genetics and Molecular Cardiology, Heart Institute (InCor), University of São Paulo Medical School, São Paulo 01246903 SP, Brazil. 32 Epidemiology and Biostatistics, University of Giorgia at Athens College of Public Health, Athens 30602 GA, USA. 33 Public Health Sciences, Biostatistical Sciences, Wake Forest University Health Sciences, Winston-Salem 27157 NC, USA. 34 Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh EH4 2XU, UK. 35 Saw Swee Hock School of Public Health, National University Health System and National University of Singapore, Singapore 117549, Singapore. 36 Icelandic Heart Association, 201 Kopavogur, Iceland. 37 Department of Biostatistics, University of Michigan, Ann Arbor 48109 MI, USA. 38 Health Disparities Research Section, Laboratory of Epidemiology and Population Sciences, National Institute on Aging, National Institutes of Health, Baltimore 21224 MD, USA. 39 Estonian Genome Center, University of Tartu, Tartu 51010, Estonia. 40 Department of Epidemiology, University of Groningen, University Medical Center Groningen, Groningen 9700 RB, The Netherlands. 41 CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille 59000, France. 42 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Stockholm 17177, Sweden. 43 Department of Biostatistical Sciences, Wake Forest School of Medicine, WinstonSalem 27157 NC, USA. 44 Department of Epidemiology and Biostatistics, Imperial College London, London W2 1PG, UK. 45 Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina 45110, Greece. 46 Molecular Genetics and Genomics Program, Wake Forest School of Medicine, Winston-Salem 27157 NC, USA. 47 Centre for Cognitive Ageing and Cognitive Epidemiology, The University of Edinburgh, Edinburgh EH8 9JZ, UK. 48 Department of Occupational and Environmental Health and State Key Laboratory of Environmental Health for Incubating, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430014, China. 49 Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor 48109 MI, USA. 50 MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0QQ, UK. 51 Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany. 52 Institute of Epidemiology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg 85764, Germany. 53 Unit of Cardiovascular Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden. 54 Department of Clinical Chemistry, Fimlab Laboratories, Tampere 33014, Finland. 55 Department of Clinical Chemistry, Finnish Cardiovascular Research Center—Tampere, Faculty of Medicine and Life Sciences, University of Tampere, Tampere 33014, Finland. 56 Foundation for Research in Health Exercise and Nutrition, Kuopio Research Institute of Exercise Medicine, Kuopio 70100, Finland. 57 College of Medicine, Biological Sciences and Psychology, Health Sciences, The Infant Mortality and Morbidity Studies (TIMMS), Leicester LE1 7RH, UK. 58 Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”, Trieste 34137, Italy. 59 Department of Computational Biology, University of Lausanne, Lausanne 1015, Switzerland. 60 Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland. 61 Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo 1628655, Japan. 62 Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Skåne University Hospital, Malmö 20502, Sweden. 63 University of Groningen, University Medical Center Groningen, Department of Cardiology, Groningen 9700 RB, The Netherlands. 64 Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor 48104 MI, USA. 65 Division of Epidemiology, Department of Medicine, Vanderbilt University School of Medicine, Nashville 37203 TN, USA. 66 Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland 44106 OH, USA. 67 Division of General Internal Medicine, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore 21287 MD, USA. 68 Dean’sOffice, University of Kentucky College of Public Health, Lexington 40536 KY, USA. 69 Human Genome Sequencing Center, Baylor College of Medicine, Houston 77030 TX, USA. 70 Department of Medical Sciences, University of Trieste, Trieste 34137, Italy. 71 Ninewells Hospital & Medical School, University of Dundee, Dundee DD1 9SY Scotland, UK. 72 Clinical Epidemiology, Leiden University Medical Center, Leiden 2300 RC, Netherlands. 73 Department of Medicine, Faculty of Medicine, University of Kelaniya, Ragama 11600, Sri Lanka. 74 Department of Internal Medicine, Section on NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-018-08008-w ARTICLE NATURE COMMUNICATIONS | (2019) 10:376 | https://doi.org/10.1038/s41467-018-08008-w | www.nature.com/naturecommunications 9